Facebook ‘Likes’ Reveal More Than You Might Think

Facebook users are inadvertently revealing many of their most personal secrets and personality traits through their “likes,” according to a University of Cambridge study that will likely raise fresh concerns about privacy and online personalization.

“This study demonstrates the degree to which relatively basic digital records of human behavior can be used to automatically and accurately estimate a wide range of personal attributes that people would typically assume to be private,” the researchers wrote in their report, which was published Monday in the journal Proceedings of the National Academy of Sciences (PNAS).

The study showed it is possible to predict private characteristics such as a person’s religion, sexual orientation, race, political views, intelligence, age, gender, emotional stability and recreational drug habits by simply analyzing information available in the public domain and linking that with a Facebook user´s public “like” updates.

The researchers conducted the study with a dataset of more than 58,000 U.S.-based volunteers who provided their Facebook “likes,” detailed demographic profiles and the results of several psychometric tests.

The researchers fed the participants´ Facebook ℠like´ data, along with other publicly available online information, into an algorithm, which in the vast majority of cases was able to accurately infer an entire range of personal traits. The predictions held true even when the participants had chosen not to publicly reveal such information.

The model correctly discriminated between homosexual and heterosexual men in 88% of cases, African Americans and Caucasian Americans in 95% of cases, and between Democrat and Republican in 85% of cases.

Christians and Muslims were correctly classified in 82 percent of cases, while relationship status was correctly predicted in 65 percent of cases and relationship status in 73 percent of cases — despite the fact that only a few users actually clicked any “like” that would explicitly reveal these attributes. For example, less that 5% of gay users clicked obvious “likes” such as gay marriage.

Instead, the algorithm relied on aggregating vast amounts of seemingly innocuous but more popular “likes,” such as music and TV shows, to produce incisive personal profiles.

A list of the most predictive “likes” for inferring IQ, happiness, religion, political leanings and other traits can be viewed here.

“What is shocking is that you can use the same data to predict your political views or your sexual orientation. This is something most people don’t realize you can do,” lead researcher Michal Kosinski told The Guardian.

Even seemingly opaque personal details, such as whether a person´s parents separated before they reached the age of 21, were accurate 60 percent of the time, enough to make the data worthwhile for advertisers, the researchers said.

The researchers said they intentionally selected traits and attributes that reveal how accurate and potentially intrusive such a predictive analysis can be.

Kosinski said he believed Facebook users would be “spooked” by the findings, and urged politicians to consider new regulations.

“I am a great fan and active user of new amazing technologies, including Facebook. I appreciate automated book recommendations, or Facebook selecting the most relevant stories for my newsfeed,” he said.

However, “users need to be provided with transparency and control over their information.”

“Just the possibility of this happening could deter people from using digital technologies and diminish trust between individuals and institutions — hampering technological and economic progress.”

“I can imagine situations in which the same data and technology is used to predict political views or sexual orientation, posing threats to freedom or even life,” he said.

For the purposes of the study, the researchers describe Facebook “likes” as a generic class of digital record, similar to web search queries and browsing histories, and suggest that such techniques could be used to extract sensitive information for almost anyone who spends time online.

“We believe that our results, while based on Facebook Likes, apply to a wider range of online behaviors,” said Kosinski, Operations Director at the Cambridge´s Psychometric Centre, who conducted the research with his Cambridge colleague David Stillwell and Thore Graepel from Microsoft Research.

“Similar predictions could be made from all manner of digital data, with this kind of secondary ‘inference’ made with remarkable accuracy – statistically predicting sensitive information people might not want revealed. Given the variety of digital traces people leave behind, it’s becoming increasingly difficult for individuals to control,” Kosinski said.

Graepel said he hoped the study would contribute to the on-going discussions about user privacy.

“Consumers rightly expect strong privacy protection to be built into the products and services they use and this research may well serve as a reminder for consumers to take a careful approach to sharing information online, utilizing privacy controls and never sharing content with unfamiliar parties.”

Although the current study used a computer algorithm to assess the participants´ personality traits, the researchers warn that the same information could be collected by anyone with training in data analysis.

Stillwell suggested that Facebook users should keep a watchful eye on their privacy settings.

“I have used Facebook since 2005, and I will continue to do so. But I might be more careful to use the privacy settings that Facebook provides,” he said.